# From AI Technical Debt to Agentic Technical Debt: A Systematic Mapping of Root Causes and Manifestations in Agentic AI Systems

arXiv: https://arxiv.org/abs/2608.01001 · HTML: https://mlsanity.com/paper/2608.01001/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 29 of its 146 references that are in the map.
- First posted on arXiv 2 Aug 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 29 of 29)

- [Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure](https://mlsanity.com/paper/2010.13561/) (2020)
- [TRiSM for Agentic AI: A Review of Trust, Risk, and Security Management in LLM-based Agentic Multi-Agent Systems](https://mlsanity.com/paper/2506.04133/) (2025)
- [Collaboration Challenges in Building ML-Enabled Systems: Communication, Documentation, Engineering, and Process](https://mlsanity.com/paper/2110.10234/) (2021)
- [Agentic AI: A Comprehensive Survey of Architectures, Applications, and Future Directions](https://mlsanity.com/paper/2510.25445/) (2025)
- [Software Engineering Challenges of Deep Learning](https://mlsanity.com/paper/1810.12034/) (2018)
- [Toward Understanding Deep Learning Framework Bugs](https://mlsanity.com/paper/2203.04026/) (2022)
- [Studying Software Engineering Patterns for Designing Machine Learning Systems](https://mlsanity.com/paper/1910.04736/) (2019)
- [Requirements Engineering Challenges in Building AI-Based Complex Systems](https://mlsanity.com/paper/1908.11791/) (2019)
- [Characterizing Technical Debt and Antipatterns in AI-Based Systems: A Systematic Mapping Study](https://mlsanity.com/paper/2103.09783/) (2021)
- [The Prevalence of Code Smells in Machine Learning projects](https://mlsanity.com/paper/2103.04146/) (2021)
- [Design Smells in Deep Learning Programs: An Empirical Study](https://mlsanity.com/paper/2107.02279/) (2021)
- [Automatic Identification of Self-Admitted Technical Debt from Four Different Sources](https://mlsanity.com/paper/2202.02387/) (2022)
- [Code Smells for Machine Learning Applications](https://mlsanity.com/paper/2203.13746/) (2022)
- [Quality issues in Machine Learning Software Systems](https://mlsanity.com/paper/2208.08982/) (2022)
- [Identifying Self-Admitted Technical Debt in Issue Tracking Systems using Machine Learning](https://mlsanity.com/paper/2202.02180/) (2022)
- [Clones in Deep Learning Code: What, Where, and Why?](https://mlsanity.com/paper/2107.13614/) (2021)
- [Data Smells in Public Datasets](https://mlsanity.com/paper/2203.08007/) (2022)
- [Maintainability Challenges in ML: A Systematic Literature Review](https://mlsanity.com/paper/2408.09196/) (2024)
- [Towards trustworthy agentic AI: a comprehensive survey of safety, robustness, privacy, and system security](https://mlsanity.com/paper/2605.23989/) (2026)
- [Data Smells: Categories, Causes and Consequences, and Detection of Suspicious Data in AI-based Systems](https://mlsanity.com/paper/2203.10384/) (2022)
- [Technical Debt in Data-Intensive Software Systems](https://mlsanity.com/paper/1905.13455/) (2019)
- [An Exploratory Study on the Introduction and Removal of Different Types of Technical Debt](https://mlsanity.com/paper/2101.03730/) (2021)
- [An architectural technical debt index based on machine learning and architectural smells](https://mlsanity.com/paper/2301.06341/) (2023)
- [Foundations for Agentic AI Investigations from the Forensic Analysis of OpenClaw](https://mlsanity.com/paper/2604.05589/) (2026)
- [Socio-Technical Anti-Patterns in Building ML-Enabled Software: Insights from Leaders on the Forefront](https://mlsanity.com/paper/2607.03270/) (2026)
- [DebtViz: A Tool for Identifying, Measuring, Visualizing, and Monitoring Self-Admitted Technical Debt](https://mlsanity.com/paper/2308.13128/) (2023)
- [SATDAUG -- A Balanced and Augmented Dataset for Detecting Self-Admitted Technical Debt](https://mlsanity.com/paper/2403.07690/) (2024)
- [Investigating Issues that Lead to Code Technical Debt in Machine Learning Systems](https://mlsanity.com/paper/2502.13011/) (2025)
- [On AI Safety and Security Technical Debt in Engineering AI-Enabled Systems](https://mlsanity.com/paper/2607.23365/) (2026)

## Abstract (arXiv)

The emergence of Agentic AI systems, characterized by autonomous reasoning, multi-agent collaboration, tool orchestration, adaptive decision-making, and persistent memory, represents a fundamental shift from traditional AI pipelines to dynamic software ecosystems. While AI Technical Debt (AITD) has been widely studied in machine learning and software engineering, existing models assume static, component-level architectures and fail to capture the dynamic and emergent behaviors of agentic environments. To address this gap, this paper introduces Agentic Technical Debt (AgTD), defined as technical debt that emerges, accumulates, propagates, and amplifies due to the autonomous and collaborative nature of Agentic AI systems. Building on our prior systematic scoping review of 31 AITDs across seven root-cause categories, we employ a theory-informed transformation methodology to reinterpret these debts in Agentic AI through direct transformation, contextual transformation, and manifestation expansion. We present the first systematic mapping of established AITDs to their agentic manifestations, showing how conventional debts evolve into system-level liabilities, including memory inconsistencies, orchestration fragility, cascading failures, and unsafe autonomous decision-making. Our findings show that technical debt extends beyond software artifacts to encompass agent behaviors, coordination mechanisms, and interactions among agents, tools, and execution environments. We further examine its implications for AI Trust, Risk, and Security Management (AI TRiSM), highlighting impacts on trustworthiness, governance, security, operational resilience, and Sustainability Technical Debt. Overall, this work establishes AgTD as a foundational software engineering construct and provides a transformation framework, taxonomy, and research agenda for managing technical debt in autonomous multi-agent AI systems.